The present invention relates generally to identity verification, and specifically to on-line determination of whether an identifying sample, such as a signature, belongs to the same owner as a plurality of reference samples.
A large amount of work has been performed regarding identity verification. In general the problem attempted to be solved in identity verification is: given a plurality of identifying reference samples and a test sample, does the test sample belong to the owner of the reference samples? The samples may be signatures, voice samples, face pictures, etc.
Due to the complexity of the human handwriting and the difference between pluralities of authentic signatures made by a given person, signature verification is very complex. The success of a solution is generally classified according to two variables: the percentage of forgeries which were identified as authentic (false positive), and the percentage of authentic signatures identified as forgeries (false negative).
Various features of signatures are used in signature verification. These features include the geometrical shape of the signature, the speed at which the signature was signed, the number of times the pen was lifted, the pressure of the pen on a pad on which the signature was signed, etc. None of these features are generally distinctive enough so that all signatures of a single individual will have the same value of the feature. Therefore, a plurality of reference signatures are taken from the individual, and the test signature is verified against the plurality of reference signatures.
xe2x80x9cProgress in Automatic signature Verificationxe2x80x9d edited by R. Plamondon, which is incorporated herein by reference, describes various methods of signature verification. A first method includes generating an average signature from the reference signatures and comparing the test signature with the average signature. Such a method is also described for example, in U.S. Pat. No. 4,040,010, which is incorporated herein by reference. In this method, data descriptive of the reference signatures is lost due to the averaging.
In a second method, described, for example, in U.S. Pat. No. 4,724,542, which is incorporated herein by reference, the test signature is compared to each of the plurality of samples and is considered authentic if it is close enough to one of the samples. In this method a large number of reference signatures are required in order to achieve suitable results.
U.S. Pat. No. 5,111,512, which is incorporated herein by reference, describes a method in which a first reference signature is chosen from a plurality of samples, and a second reference signature is constructed by averaging the samples. Some features of the test signature are compared to the first reference signature, while others are compared to the second reference signature. However, this method still does not overcome the problem of information loss due to the averaging.
U.S. Pat. No. 5,680,470, which is incorporated herein by reference, describes a method in which the features of reference signatures are stored in an associative memory. A test signature is compared to the reference signatures using the associative memory. However, the features of the reference signatures may be so diverse as not to allow the associative memory to generate internal rules governing the differences between true and false signatures. For better results, it is desirable to more closely define for the associative memory the points of comparison between the reference signatures and the test signature.
It is an object of some aspects of the present invention to provide methods for determination whether a given test sample belongs to the same person as a plurality of reference samples.
It is another object of some aspects of the present invention to provide methods for reliable determination of whether a given test sample belongs to a given person using a relatively low number of reference samples.
It is another object of some aspects of the present invention to provide a relatively simple method and apparatus for determining whether a signature is authentic.
It is another object of some aspects of the present invention to provide methods and apparatus for fast determination of whether a signature is authentic.
In preferred embodiments of the present invention, a processor receives a plurality of reference samples and a test sample, for example, samples of a person""s signature, and calculates a correlation value between each pair of the samples for each of one or more features of the samples which are identified by or input to the processor. For each of the one or more features, a matrix of the correlation values is formed, and the processor evaluates the regularity of the matrix. The test sample is labeled as authentic if the matrix has a predetermined measure of regularity. In other words, the test sample is identified as authentic if the regularity of its correlation with the reference samples is similar to the regularity of the correlation of the reference samples with one another. Thus, unlike methods of signature identification known in the art, the method used in preferred embodiments of the present invention identifies the test sample as authentic not if it is closely correlated with one of the reference samples, but rather if its degree of correlation (and deviation from full correlation) with the reference samples is comparable to the degree of correlation between the reference samples.
In some preferred embodiments of the present invention, the regularity of the matrix is evaluated by forming a set of points in an n-Euclidean space (n being the number of reference samples) which is representative of the matrix, and evaluating the regularity of the points of the set. Preferably, the points of the set are generated such that the distances between the points are the correlation values of the matrix. Preferably, the set includes n points representative of the reference samples and one test point representative of the test sample.
In some preferred embodiments of the present invention, the regularity of the points of the set is evaluated by evaluating the regularity of a test polyhedral formed of the points. Preferably, in order to establish authenticity of the test sample, the test polyhedral must have a shape resembling a reference polyhedral formed from the points corresponding to the reference samples. Preferably, one or more parameters representative of the relation between the test point and the test polyhedral are calculated. The one or more parameters are preferably normalized by dividing them by an average of the values of the parameters calculated for each of the reference points corresponding to the reference samples. The normalized parameters of each set of points representing a particular feature are used to determine whether the test sample is authentic.
In some preferred embodiments of the present invention, the one or more parameters comprise two parameters: a height of the polyhedral, and a variance of the test point relative to the polyhedral.
In some preferred embodiments of the present invention, the determination whether the sample is authentic is performed by providing the one or more parameters of each of the one or more features to a suitably trained neural network. Preferably, the neural network comprises a Boltzmann Perceptron classifier, although other networks may be used. Alternatively or additionally, other classifiers may be used instead of neural networks, such as a decision tree (for example, an ID3-type tree), as is known in the art.
In some preferred embodiments of the present invention, the samples comprise signatures of a human individual. Preferably, the features of the signatures comprise features indicative of the correlation between two signatures. Therefore, the correlation values of the matrix are given by the values of the features. Preferably, the features include an absolute speed mismatch, an average speed mismatch, a uniformity of point-to-point correspondence, and a shape difference, all of which are calculated based on measurements made on the two signatures in accordance with methods of signature identification known in the art. Preferably, the features further include the lengths of the two signatures. Any other features known in the art of signature identification and authentication may similarly be used for this purpose.
Alternatively or additionally, the features may include features of single signatures, such as any of the 25 features mentioned on page 128 of the aforementioned xe2x80x9cProgress in Automatic Signature Verification.xe2x80x9d The correlation values used as the terms of a matrix relating to a single feature are therefore equivalent to the differences between the values of the feature for each respective pair of signatures. Alternatively or additionally, other functions of the features of the pair of signatures, such as the minimum or average of the feature values, may be used to form the terms of the matrix.
In some preferred embodiments of the present invention, the processor receives more than n reference samples and discards one or more of the samples so as to remain with n reference samples. Preferably, the discarded samples are chosen as the samples most inconsistent with the rest of the reference samples.
There is therefore provided in accordance with a preferred embodiment of the present invention, a method of determining whether a test sample belongs to the same individual as a plurality of reference samples, including providing respective values of one or more identifying features from the test sample and the plurality of reference samples, presenting the test and reference samples by respective points having coordinates in a multi-dimensional space indicative of the respective feature values, and deciding whether the test sample belongs to the same individual as the plurality of reference samples based on a geometrical property of the points.
Preferably, providing the respective values includes extracting from the samples a plurality of values of the one or more features descriptive of the samples, and representing the samples by one or more geometric points includes representing each sample by one point for each of the one or more features.
Preferably, at least some of the one or more features include a value dependent on a comparison of two of the samples.
Preferably, representing each of the test and reference samples by one or more geometric points includes generating for each of the one or more features, a square matrix in which each row and each column is representative of one of the samples, and in which the values of the terms of the matrix are responsive to the value of the feature of the samples of the row and column of the term, and generating for each matrix a plurality of points representative of the matrix.
Preferably, generating the plurality of points representative of the matrix includes smoothing the matrix and generating the points based on the smoothed matrix.
Preferably, smoothing the matrix includes applying a square root operator to the terms of the matrix.
Alternatively or additionally, smoothing the matrix includes generating a symmetrical matrix responsive to the matrix to be smoothed.
Preferably, representing the test and reference samples by respective points includes generating the points such that the distances between the points are proportional to the respective values of the features.
Preferably, deciding based on the geometrical property of the points includes deriving a parameter indicative of the regularity of a polyhedron formed from the points.
Preferably, deriving the parameter includes deriving parameters indicative of the relative form of a polyhedron including points representing both the test and reference samples compared to a polyhedron including points representing only the reference samples.
Further preferably, deriving parameters indicative of the relative form of the polyhedrons includes deriving relative heights and variances of the polyhedrons.
Preferably, deciding whether the test sample belongs to the same individual as the reference samples includes inputting the parameters to a classifier.
Preferably, the classifier includes a neural network.
Preferably, the samples include signatures.
Preferably, providing the respective values includes performing a dynamic time warping technique on the samples.
Preferably, providing the respective values includes determining a speed mismatch of the samples.
Preferably, representing the test and reference samples by the respective points includes representing the samples by n-dimensional points wherein n is the number of reference. samples.
Preferably, representing the test and reference samples by the respective points includes representing the samples such that each of the points has a different number of non-zero coordinates.
Further preferably, the test sample is represented by a point substantially all of whose coordinates are non-zero.
Preferably, the method includes eliminating one or more of the reference samples prior to representing the test and reference samples by the respective points.
There is further provided in accordance with a preferred embodiment of the present invention, apparatus for determining whether a test sample belongs to the same individual as a plurality of reference samples, including an input device which acquires the samples a processor which determines values of one or more features of the samples, represents the samples by corresponding points in a multi-dimensional space having coordinates indicative of the feature values, and calculates a geometrical property of the points, and a classifier which determines whether the test sample belongs to the same individual as the plurality of reference samples responsive to the geometrical property.
Preferably, the samples include signatures, and the input device includes a writing implement.
Preferably, the input device does not determine a pressure of the writing implement on a pad.
Preferably, the classifier includes a neural network.
Preferably, the classifier is implemented by software in the processor.
Preferably, the processor represents each sample by one point for each of the one or more features.
Preferably, the processor determines values of the one or more features using a dynamic time warping technique.
Preferably, the processor calculates the geometrical property by deriving parameters indicative of the relative form of a polyhedron including points representing both the test and reference samples compared to a polyhedron including points representing only the reference samples.
There is also provided, in accordance with a preferred embodiment of the present invention, a computer program product having computer readable program code embodied therein, which code causes a processor receiving a test sample and a plurality of reference samples, wherein each sample is characterized by respective values of one or more identifying features, to represent the test and reference samples by respective points having coordinates in a multi-dimensional space indicative of the respective feature values, and to decide whether the test sample belongs to the same individual as the plurality of reference samples based on a geometrical property of the points.